School admission prediction system and method

Abstract

A school admission prediction system and method are provided. The system includes a user interface for receiving personal data from an applicant, including academic and activity data. A data acquisition module connected to the user interface acquires this personal data. A data preprocessing module connected to the data acquisition module preprocesses the academic and activity data. An attribute selection module connected to the data preprocessing module extracts multiple attributes from the preprocessed data. A machine learning model generates an evaluation report based on the extracted attributes. This report includes a prediction of whether the applicant will be admitted to the school. The system also includes a loss calculation module for evaluating the performance of the machine learning model and optimizing its parameters based on the evaluation results. The method and system provide a reliable and efficient way to predict school admissions, helping applicants to better prepare their applications.

What is claimed is: 
     
         1 . A school admission prediction system for assessing personal data of at least one applicant to determine if the applicant will be admitted to a school, the school admission prediction system comprising:
 a user interface for the applicant to input their personal data and at least one desired school for admission, wherein the personal data includes at least one academic data and at least one activity data;   a data acquisition module, connected to the user interface, configured to acquire personal data;   a data preprocessing module, connected to the data acquisition module, configured to pre-process the academic data and activity data of the applicant;   an attribute selection module, connected to the data preprocessing module, configured to extract multiple attributes from the preprocessed academic data and activity data; and   a machine learning model, connected to the attribute selection module and the user interface, configured to generate an evaluation report based on the attributes transmitted from the attribute selection module, wherein the evaluation report includes an assessment of whether the applicant can be admitted to the desired school, and the machine learning model sends the evaluation report to the user interface for display;   wherein the machine learning model further includes a loss calculation module, and a training process of the machine learning model includes a plurality of steps:   connecting the data acquisition module of the school admission prediction system to a database, wherein the database stores multiple previous application data, each of the previous application data includes academic data and activity data of previous applicants, the schools applied by the previous applicants, and admission data;   retrieving the previous application data from the database by the data acquisition module;   preprocessing the retrieved previous application data by preprocessing module;   extracting multiple attributes from the preprocessed previous application data by the attribute selection module;   training the machine learning model using the extracted attributes and admission data;   evaluating the performance of the machine learning model and generating an evaluation result through the loss calculation module, optimizing the parameters of the machine learning model according to the evaluation result until the evaluation result is less than a predetermined threshold.   
     
     
         2 . The school admission prediction system as claimed in  claim 1 , wherein the attribute selection module is further configured to evaluate the importance of each attribute during the training process of the machine learning model, based on feedback from the machine learning model, and the attribute is retained if the feedback indicates that the attribute is important or removed if the feedback indicates that the attribute is not important. 
     
     
         3 . The school admission prediction system as claimed in  claim 2 , wherein the attribute selection module can also derive new attributes based on the preprocessed data. 
     
     
         4 . The school admission prediction system as claimed in  claim 1 , wherein the attribute selection module can also derive new attributes based on the preprocessed data. 
     
     
         5 . The school admission prediction system as claimed in  claim 1 , wherein the machine learning model is a multilayer perceptron model. 
     
     
         6 . The school admission prediction system as claimed in  claim 1 , wherein the attributes include at least one of the following: average GPA, volunteer work, work experience, extracurricular activities, interests of the applicant, and standardized test scores. 
     
     
         7 . The school admission prediction system as claimed in  claim 6 , wherein the attributes further include at least one of the following: the applicant's gender, the applicant's nationality, and the admission rate of the school. 
     
     
         8 . The school admission prediction system as claimed in  claim 1 , wherein the attribute selection module can also derive new attributes based on the preprocessed data. 
     
     
         9 . A method for predicting school admissions, comprising:
 receiving personal data from at least one applicant through a user interface, wherein the personal data includes at least one piece of academic data and at least one piece of activity data;   acquiring the personal data through a data acquisition module connected to the user interface;   preprocessing the academic data and activity data of the applicant through a data preprocessing module connected to the data acquisition module;   extracting multiple attributes from the preprocessed academic data and activity data through an attribute selection module connected to the data preprocessing module;   a machine learning model generating an evaluation report based on the attributes transmitted from the attribute selection module, wherein the evaluation report includes an assessment of whether the applicant can be admitted to the school, and transmitting the evaluation report to the user interface for display;   wherein a training process of the machine learning model includes the following steps:   connecting the data acquisition module of the school admission prediction system to a database, where the database stores multiple pieces of previous application data, each piece of previous application data includes at least: academic data and activity data of the previous applicant, the schools applied to by the previous applicant, and admission data;   retrieving the previous application data from the database through the data acquisition module; and   preprocessing the retrieved application data through the data preprocessing module;   extracting multiple attributes from the preprocessed application data through the attribute selection module;   training the machine learning model using the extracted attributes and admission data;   evaluating the performance of the machine learning model and generating an evaluation result through a loss calculation module, optimizing the parameters of the machine learning model according to the evaluation result until the evaluation result is less than a predetermined threshold.   
     
     
         10 . The school admission prediction method as claimed in  claim 9 , wherein the attribute selection module further evaluates the importance of each attribute during the training process of the machine learning model, based on feedback from the machine learning model, and the attribute is retained if the feedback indicates that the attribute is important or removed if the feedback indicates that the attribute is not important. 
     
     
         11 . The school admission prediction method as claimed in  claim 9 , wherein the machine learning model is a multilayer perceptron model. 
     
     
         12 . The school admission prediction method as claimed in  claim 9 , wherein the attributes include at least one of the following: average GPA, extracurricular activities, volunteer work, work experience, interests of the applicant, and standardized test scores. 
     
     
         13 . The school admission prediction method as claimed in  claim 12 , wherein the attributes further include at least one of the following: the applicant's gender, the applicant's nationality, and the school's admission rate.